Search Results (10)

Extant empirical studies investigate social commerce purchase intention from the perspective of swift guanxi dimensions while neglecting to explain how the purchase intention is influenced. This study proposed and tested a research model to unveil the relationship between swift guanxi dimensions (mutual understanding, reciprocity favor, and relationship harmony), trust in the seller and purchase intention while considering the mediation effect of trust in the seller in social commerce settings. Data from 421 social commerce youth consumers in Tanzania were used in PLS-SEM analysis to test the research model. Results revealed that except for reciprocity favor, swift guanxi dimensions positively influence purchase intention. The swift guanxi dimensions also positively influence trust in the seller. Further trust in the seller mediates the relationship between swift guanxi dimensions and purchase intention. This study recommends that social commerce micro, small, and medium traders embrace swift guanxi to drive consumer purchase intention. Swift guanxi dimensions foster a rapid and affirmative connection with the consumers, enhancing their trust in the seller. In turn, trust in the seller significantly enhances the likelihood of purchase intention, since the consumers feel safer and more confident in their buying journey. Therefore, by leveraging swift guanxi dimensions, social commerce sellers can effectively build business relationships based on a strong foundation, which not only drives immediate consumer purchases but also fosters enduring consumer devotion. Full article

New Zealand (NZ) construction is highly fragmented, comprises primarily micro small and medium enterprises (MSMEs); 32.5% of approximately 67,000 operating businesses are ‘small’ (up to 19 employees), while 65% are sole traders. The construction supply chain (CSC) is extensive, prone to inefficiencies at segmental boundaries accentuated by project-centric delivery. Conversely, it presents significant opportunity for consolidation and improved efficiency. Vertical integration and CSC management from the supplier-end rather than the project-end enable component elements to be individually independent in terms of ownership, while integrating their management above the tactical CSC level. This leads to improved operational philosophy and employment. Quantifying impacts, however, is a challenge due to lack of tangibility. This can be effectively overcome using quantifiable parameters associated with the CSC’s transport component. The paper investigates transport operations in a narrow NZ CSC segment over a three-month period to quantify improved performance using operational data and further potential for resource optimisation using operations research-based planning. Research outcomes point towards: (i) Fleet management strategy; (ii) Integrated planning and operational delivery; (iii) Non-price attributes in tendering/contracting; (iv) Change in the delivery model of manufactured construction products; (v) Information and communication technology-based solutions; and (vi) Integration of reverse logistics. Full article

The coronavirus disease 2019 (COVID-19) pandemic that struck unexpectedly has reinforced the need for proper cash flow management by micro-businesses. It may be complicated for the business sector to differentiate between business and personal finances if there is no discipline or consistency in financial management. Therefore, this study examines the practice of cash flow management by micro-traders in Kuala Terengganu, Malaysia, before 2020 and examines how their lives were affected during the freeze on economic activity. A quantitative research design was adopted, and the participants for the study were selected using the convenience sampling technique. A survey was conducted in November 2021, and 141 pieces of clean data were obtained. Statistical Package for the Social Sciences software (SPSS) was used to generate descriptive statistics for data analysis. This study determined that micro-traders did not exhibit proper cash flow management, placing them in a critical financial situation when the pandemic crisis was prolonged. The movement control order and the extended COVID-19 pandemic that caused the economic downturn negatively impacted the cash flow of micro-traders and negatively affected their incomes and livelihoods. Full article

Taxation is a powerful tool to achieve sustainable development goals (SDG) as tax policies help strengthen economic growth and improve revenue capacity. So, after tax reform, it is vital to analyze their impact on the performance of enterprises. Keeping in mind the SDG, the present study was conducted in India after the major tax overhaul—Goods and Service Tax (GST) on 1 July 2017, to measure the impact on Return on Equity (ROE) and Return on Investment (ROI) as they are the barometers to measure performance (DuPont Analysis). We opted for tax reform determinants, the characteristics of firms, demographic variables, and drivers from DuPont analysis to conduct the research, as all these variables can help identify the different causes of factors impacting ROI and ROE among different types of firms and demographics across countries. An econometric analysis of 546 registered SMEs (small and micro enterprises) was conducted using the regression model, structured equation modeling, and exploratory and confirmatory factor analysis to achieve the objectives. The empirical findings highlighted that a firm’s size, turnover, and DuPont analysis drivers (earnings × asset to sales × asset turnover) positively enhanced the ROI and ROE. Further, the change in the tax system after the tax reforms has enabled the detection of tax fraud and wrong invoices, reducing the missing insolvent traders and increasing the working capital flow of the firms, which in turn has augmented financial performance. Full article

Individual mental feelings and reactions are getting more significant as they help researchers, domain experts, businesses, companies, and other individuals understand the overall response of every individual in specific situations or circumstances. Every pure and compound sentiment can be classified using a dataset, which can be in the form of Twitter text by various Twitter users. Twitter is one of the vital platforms for individuals to participate and share their ideas about different topics; it is also considered to be one of the most famous and the biggest website for micro-blogging on the Internet. One of the key purposes of this study is to classify pure and compound sentiments based on text related to cryptocurrencies, an innovative way of trading and flourishing daily. The cryptocurrency market incurs many fluctuations in the coins’ value. A small positive or negative piece of news can sensate the whole scenario about the specific cryptocurrencies. In this paper, individuals’ pure and compound sentiments based on cryptocurrency-related Twitter text are classified. The dataset is collected through the Twitter API. In WEKA, the two deployment schemes are compared; firstly, straight with single feature selection technique (Tweet to lexicon feature vector), and secondly, a tetrad of feature selection techniques (Tweet to lexicon feature vector, Tweet to input lexicon feature vector, Tweet to SentiStrength feature vector, and Tweet to embedding feature vector) are used to purify the data LibLINEAR (LL) classifier, which contains fast algorithms for linear classification using L2-regularization L2-loss support vector machines (Dual SVM). The LL classifier differs in that it can potentially alleviate the sum of the absolute values of errors rather than the sum of the squared errors and is typically much speedier. Based on the overall performance parameters, the deployment scheme containing the tetrad of feature selection techniques with the LL classifier is considered the best choice for the purpose of classification. Among machine learning techniques, LL produces effective results and gives an efficient performance compared to other prevailing techniques. The findings of this research would be beneficial for Twitter users as well as cryptocurrency traders. Full article

This study used social network analysis, which is often adopted to analyze changes in trade structures and the world trade network for faux fur products, which are alternative materials used in vegan fashion. The data on the total trade value of artificial fur (HS Code: 4304) and animal fur and leather (HS Code: 0505) imports and exports between countries were collected through UN Comtrade, and the degree and betweenness centralities were used to analyze the trade structure of faux fur in 2009, 2014, and 2019 using NodeXL 1.0.1 programs. The results of the study are as follows: First, while the total amount of export and import of faux fur is increasing globally every day, the total amount of export and import in other Asian countries and Vietnam is decreasing. Second, due to the reduction in exports of the main producing countries of animal materials such as China, global imports of animal fur and leather decreased. Third, China was the largest ex-porter of faux fur, exporting to a large number of countries; it also played an important role in the intermediation and control over faux fur export. In exporting faux fur, the influence of other Asian countries declined over time, and Vietnam and the United States played an outstanding role as arbitrators in the export network. Fourth, Italy and France were the largest importers of faux fur from various countries and exerted significant influence as intermediaries in the import network of faux fur. On the other hand, Vietnam’s influence in import network decreased. Saudi Arabia appeared to be an important arbitrator in mediating the import. This study is significant due to its findings, obtained through micro-trading data, in respect of industrial moves of ethical fashion in the form of increased trade in faux fur and decline in the trade of animal fur and leather. Full article

Water availability is increasingly stressed in cities across the world due to population growth, which increases demands, and climate change, which can decrease supply. Novel water markets and water supply paradigms are emerging to address water shortages in the urban environment. This research develops a new peer-to-peer non-potable water market that allows households to capture, use, sell, and buy rainwater within a network of water users. A peer-to-peer non-potable water market, as envisioned in this research, would be enabled by existing and emerging technologies. A dual reticulation system, which circulates non-potable water, serves as the backbone for the water trading network by receiving water from residential rainwater tanks and distributing water to households for irrigation purposes. Prosumer households produce rainwater by using cisterns to collect and store rainwater and household pumps to inject rainwater into the network at sufficiently high pressures. The smart water grid would be enabled through an array of information and communication technologies that provide capabilities for automated and real-time metering of water flow, control of infrastructure, and trading between households. The goal of this manuscript is to explore and test the hydraulic feasibility of a micro-trading system through an agent-based modeling approach. Prosumer households are represented as agents that store rainwater and pump rainwater into the network; consumer households are represented as agents that withdraw water from the network for irrigation demands. An all-pipe hydraulic model is constructed and loosely coupled with the agent-based model to simulate network hydraulics. A set of scenarios are analyzed to explore how micro-trading performs based on the level of irrigation demands that could realistically be met through decentralized trading; pressure and energy requirements at prosumer households; pressure and water quality in the pipe network. Full article

Avian influenza virus (AIV) remains a huge challenge for poultry production with negative repercussions for micro- and macro-economy and public health in Bangladesh. High (HP) H5N1 and low pathogenicity (LP) H9N2 AIV are currently endemic in poultry, and both have been reported to infect humans sporadically. Multiple virus introductions of different clades of HPAIV H5N1, reassorted genotypes, and on-going diversification of LPAIV H9N2 create a highly volatile virological environment which potentially implicates increased virulence, adaptation to new host species, and subsequent zoonotic transmission. Allotropy of poultry rearing systems and supply chains further increase the risk of virus spreading, which leads to human exposure and fosters the emergence of new potentially pre-pandemic virus strains. Here, we review the epidemiology, focusing on (i) risk factors for virus spreading, (ii) viral genetic evolution, and (iii) options for AIV control in Bangladesh. It is concluded that improved control strategies would profit from the integration of various intervention tools, including effective vaccination, enhanced biosecurity practice, and improved awareness of producers and traders, although widespread household poultry rearing significantly interferes with any such strategies. Nevertheless, continuous surveillance associated with rapid diagnosis and thorough virus characterization is the basis of such strategies. Full article

The machine-learning paradigm promises traders to reduce uncertainty through better predictions done by ever more complex algorithms. We ask about detectable results of both uncertainty and complexity at the aggregated market level. We analyzed almost one billion trades of eight currency pairs (2007–2017) and show that increased algorithmic trading is associated with more complex subsequences and more predictable structures in bid-ask spreads. However, algorithmic involvement is also associated with more future uncertainty, which seems contradictory, at first sight. On the micro-level, traders employ algorithms to reduce their local uncertainty by creating more complex algorithmic patterns. This entails more predictable structure and more complexity. On the macro-level, the increased overall complexity implies more combinatorial possibilities, and therefore, more uncertainty about the future. The chain rule of entropy reveals that uncertainty has been reduced when trading on the level of the fourth digit behind the dollar, while new uncertainty started to arise at the fifth digit behind the dollar (aka ‘pip-trading’). In short, our information theoretic analysis helps us to clarify that the seeming contradiction between decreased uncertainty on the micro-level and increased uncertainty on the macro-level is the result of the inherent relationship between complexity and uncertainty. Full article

Local marketplaces are remarkable organizations for agricultural product transactions in Ethiopia. However, little is known concerning measurement practices in these micro-trading zones. Thus, this study intended to examine the cereal commodity quantity measurement behaviors of farmers in the local marketplaces of Ethiopia. A survey was conducted in four districts marketplaces (N = 382) of the Oromia Region. The χ² test was employed to evaluate the association between farmers’ perception of the accuracy of local units and measuring instruments related to immoral buyers’ behaviors. According to the findings, farmers’ cereals quantity measurement behaviors proved the presence of unreliability which created transaction, measurement, social capital, and two-hand palm cereals gift costs. The χ² test results indicated that farmers’ perceptions of the accuracy of local units and measuring tools related to buyers’ unethical behaviors had significant relationships with bowl, glass, sack, and can local units, except for weight balance in Dendi and Bako Tibe, and for cans in the Gimbichu area. This study demonstrates that standardization of tools and measurements, together with institutional support, would have a huge potential for economizing transaction costs and making equitable cereals exchanges and efficient markets. Full article